Search results for: fuzzy cluster analysis

Authors: Ahmet Kayabasi, Ali Akdagli

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In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.

Keywords: a-shaped compact microstrip antenna, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM)

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Authors: M. Abbasi Layegh, S. Haghipour, K. Athari, R. Khosravi, M. Tafkikialamdari

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An approach to find the optimum mel-frequency cepstral coefficients (MFCCs) for the Radif of Mirzâ Ábdollâh, which is the principal emblem and the heart of Persian music, performed by most famous Iranian masters on two Iranian stringed instruments ‘Tar’ and ‘Setar’ is proposed. While investigating the variance of MFCC for each record in themusic database of 1500 gushe of the repertoire belonging to 12 modal systems (dastgâh and âvâz), we have applied the Fuzzy C-Mean clustering algorithm on each of the 12 coefficient and different combinations of those coefficients. We have applied the same experiment while increasing the number of coefficients but the clustering accuracy remained the same. Therefore, we can conclude that the first 7 MFCCs (V-7MFCC) are enough for classification of The Radif of Mirzâ Ábdollâh. Classical machine learning algorithms such as MLP neural networks, K-Nearest Neighbors (KNN), Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and Support Vector Machine (SVM) have been employed. Finally, it can be realized that SVM shows a better performance in this study.

Keywords: radif of Mirzâ Ábdollâh, Gushe, mel frequency cepstral coefficients, fuzzy c-mean clustering algorithm, k-nearest neighbors (KNN), gaussian mixture model (GMM), hidden markov model (HMM), support vector machine (SVM)

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Authors: V. Nagammai

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Very-large-scale integration (VLSI) is the process of creating an integrated circuit (IC) by combining thousands of transistors into a single chip. Development of technology increases the complexity in IC manufacturing which may vary the power consumption, increase the size and latency period. Topology defines a number of connections between network. In this project, NoC topology is generated using atlas tool which will increase performance in turn determination of constraints are effective. The routing is performed by XY routing algorithm and wormhole flow control. In NoC topology generation, the value of power, area and latency are predetermined. In previous work, placement, routing and shortest path evaluation is performed using an algorithm called floor planning with cluster reconstruction and path allocation algorithm (FCRPA) with the account of 4 3x3 switch, 6 4x4 switch, and 2 5x5 switches. The usage of the 4x4 and 5x5 switch will increase the power consumption and area of the block. In order to avoid the problem, this paper has used one 8x8 switch and 4 3x3 switches. This paper uses IPRCA which of 3 steps they are placement, clustering, and shortest path evaluation. The placement is performed using min – cut placement and clustering are performed using an algorithm called cluster generation. The shortest path is evaluated using an algorithm called Dijkstra's algorithm. The power consumption of each block is determined. The experimental result shows that the area, power, and wire length improved simultaneously.

Keywords: application specific noc, b* tree representation, floor planning, t tree representation

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Authors: Yeoju Chung

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Adolescents internet and smartphone addiction are increasing in Korea. But differences between internet addiction and smartphone addiction have been researched in these days. The main objective of this article is to explore the presence of clusters within a sample of adolescents based on dimensions associated with addiction and internet usage needs. The sample consists of 617 adolescents in the 14-19 year age group who were recruited in Korea A cluster analysis identified four groups of participants: internet overuse(IO), smartphone overuse(SO), both overuse(B) and normal(N) use group. MANOVA analysis based on internet usage showed that there are differences among four groups in internet usage needs. IO has higher cyber self-seeking needs and emotion and thought expression needs than SO. SO has higher real relationship and life needs with cyberworld than IO, B, and N. B has the highest cyber self-seeking needs and emotion and thought expression needs, however, game fun seeking needs is the highest in IO. These results support that IO seeks game fun needs, SO seeks real relationship and life needs, and B seeks cyber self and expression in cyberworld.

Keywords: addiction, internet, needs, smartphone

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Authors: Guilherme Reis Ranieri, Guilherme Leite Gaudereto, Michele Toledo, Luis Fernando Amato-Lourenco, Thais Mauad

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Given the importance to urban sustainability and the growing relevance of the term ‘community garden’, this paper aims to conduct a bibliometric analysis of the term. Using SCOPUS as database, we analyzed 105 articles that contained the keywords ‘community garden’, and conducted a cluster analysis with the associated keywords. As results, we found 205 articles and 404 different keywords. Among the keywords, 334 are not repeated anytime, 44 are repeated 2 times and 9 appear 3 times. The most frequent keywords are: community food systems (74), urban activism (14), Communities of practice (6), food production (6) and public rethoric (5). Within the areas, which contains more articles are: social sciences (74), environmental science (29) and agricultural and biological sciences (24).The three main countries that concentrated the papers are United States (54), Canada (15) and Australia (12). The main journal with these keywords is Local Environment (10). The first publication was in 1999, and by 2010 concentrated 30,5% of the publications. The other 69,5% occurred 2010 to 2015, indicating an increase in frequency. We can conclude that the papers, based on the distribution of the keywords, are still scattered in various research topics and presents high variability between subjects.

Keywords: bibliometrics, community garden, metrics, urban agriculture

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Authors: Uzma Ayaz, Sanam Bashir, Shahid Iqbal Awan, Muhammad Ilyas, Muhammad Fareed Khan

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Drought stress directly affects growth along with the productivity of plants by altering plant water status. Sunflower (Helianthus annuus L.), an oilseed crop, is adversely affected by abiotic stresses. The present study was carried out to characterize the genetic variability for seedling and morpho-physiological parameters in different sunflower genotypes under water-stressed conditions. A total of twenty-seven genotypes, including two hybrids, eight advanced lines and seventeen accessions of sunflower (Helianthus annuus L.) were tested against drought stress at Seedling stages by Polyethylene glycol (PEG). Significant means were calculated among traits using analysis of variance (ANOVA) whereas, correlation and principal component analysis also confirmed that germination percentage, root length, shoot length, chlorophyll content, stomatal frequency are positively linked with each other hence, these traits were responsible for most of the variation among genotypes. The cluster analysis results showed that genotypes Ausun, line-3, line-2, and 17578, line-1, line-7, line-6 and 17562 as more diverse among all the genotypes. These most divergent genotypes could be utilized in the development of drought-tolerant inbreed lines which could be subsequently used in future heterosis breeding programs.

Keywords: sunflower, drought, stress, polyethylene- glycol, screening

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Authors: Gu Sihao

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The coordinated development of "ecology-production-life" in cities has been highly concerned by the country, and the transformation development and sustainable development of resource-based cities have become a hot research topic at present. As an important part of China's resource-based cities, coal resource-based cities have the characteristics of large number and wide distribution. However, due to the adjustment of national energy structure and the gradual exhaustion of urban coal resources, the development vitality of coal resource-based cities is gradually reduced. In many studies, the deterioration of ecological environment in coal resource-based cities has become the main problem restricting their urban transformation and sustainable development due to the "emphasis on economy and neglect of ecology". Since the 18th National Congress of the Communist Party of China (CPC), the Central Government has been deepening territorial space planning and development. On the premise of optimizing territorial space development pattern, it has completed the demarcation of ecological protection red lines, carried out ecological zoning and ecosystem evaluation, which have become an important basis and scientific guarantee for ecological modernization and ecological civilization construction. Grasp the regional multiple ecosystem services is the precondition of the ecosystem management, and the relationship between the multiple ecosystem services study, ecosystem services cluster can identify the interactions between multiple ecosystem services, and on the basis of the characteristics of the clusters on regional ecological function zoning, to better Social-Ecological system management. Based on this cognition, this study optimizes the spatial function zoning of Gujiao, a coal resource-based city, in order to provide a new theoretical basis for its sustainable development. This study is based on the detailed analysis of characteristics and utilization of Gujiao city land space, using SOFM neural networks to identify local ecosystem service clusters, according to the cluster scope and function of ecological function zoning of space partition balance and coordination between different ecosystem services strength, establish a relationship between clusters and land use, and adjust the functions of territorial space within each zone. Then, according to the characteristics of coal resources city and national spatial function zoning characteristics, as the driving factors of land change, by cellular automata simulation program, such as simulation under different restoration strategy situation of urban future development trend, and provides relevant theories and technical methods for the "third-line" demarcations of Gujiao's territorial space planning, optimizes territorial space functions, and puts forward targeted strategies for the promotion of regional ecosystem services, providing theoretical support for the improvement of human well-being and sustainable development of resource-based cities.

Keywords: coal resource-based city, territorial spatial planning, ecosystem service cluster, gmop model, geosos-FLUS model, functional zoning optimization and upgrading

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Authors: Ma. Cecilia D. Licuan

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The pandemic situation continues to affect the lives of many people, including children with disabilities and their families, globally, especially in developing countries like the Philippines. The operations of health programs, industries, and economic sectors, as well as academic training institutions, are still challenged in terms of operations and delivery of services. The academic community of the Physical Therapy program is not spared by this circumstance. The restriction posted by the quarantine policies nearly terminated the onsite delivery of training programs for the senior internship level, which challenged the academic institutions to implement flexible learning programs to ensure the continuity of the instructional and learning processes with full consideration of safety and compliance to health protocols. This study aimed to develop a benchmark model that can be used by tertiary-level health institutions in the implementation of the Pediatric Senior Clinical Internship Training Program using Telerehabilitation. It is a descriptive-qualitative paper that utilized documentary analysis and focused on explaining the design, development, and implementation processes used by De La Salle Medical and Health Sciences Institute – College of Rehabilitation Sciences (DLSMHSI-CRS) Physical Therapy Department in its Pediatric Cluster Senior Clinical Internship Training Program covering the pandemic years spanning from the academic year 2020- 2021 to present anchored on needs analysis based on documentary reviews. Results of the study yielded the determination of the Pediatric Telerehabilitation Model; declaration of developed training program outcomes and thrusts and content; explanation of the process integral to the training program’s pedagogy in implementation; and the evaluation procedures conducted for the program. Since the study did not involve human participants, ethical considerations on the use of documents for review were done upon the endorsement of the management of the DLSMHSI-CRS to conduct the study. This paper presents the big picture of how a tertiary-level health sciences institution in the Philippines embraced the senior clinical internship challenges through the operations of its telerehabilitation program. It specifically presents the design, development and implementation processes used by De La Salle Medical and Health Sciences Institute – College of Rehabilitation Sciences Physical Therapy Department in its Pediatric Cluster Senior Clinical Internship Training Program, which can serve as a benchmark model for other institutions as they continue to serve their stakeholders amidst the pandemic.

Keywords: pediatric physical therapy, telerehabilitation, clinical internship, pandemic

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Authors: Marit D. Murry, Amy K. Marks

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The transition to college is a critical period that affords abundant opportunities for growth in conjunction with novel challenges for emerging adults. During this time, emerging adults are garnering experiences and acquiring hosts of new information that they are required to synthesize and use to inform life-shaping decisions. This stage is characterized by instability and exploration, which necessitates a diverse set of coping skills to successfully navigate and positively adapt to their evolving environment. However, important sociocultural factors result in differences that occur developmentally for minority emerging adults (i.e., emerging adults with an identity that has been or is marginalized). While the transition to college holds vast potential, not all are afforded the same chances, and many individuals enter into this stage at varying degrees of readiness. Understanding the nuance and diversity of student preparedness for college and contextualizing these factors will better equip systems to support incoming students. Emerging adulthood for ethnic, racial minority students presents itself as an opportunity for growth and resiliency in the face of systemic adversity. Ethnic, racial identity (ERI) is defined as an identity that develops as a function of one’s ethnic-racial group membership. Research continues to demonstrate ERI as a resilience factor that promotes positive adjustment in young adulthood. Adaptive coping responses (e.g., engaging in help-seeking behavior, drawing on personal and community resources) have been identified as possible mechanisms through which ERI buffers youth against stressful life events, including discrimination. Additionally, trait mindfulness has been identified as a significant predictor of general psychological health, and mindfulness practice has been shown to be a self-regulatory strategy that promotes healthy stress responses and adaptive coping strategy selection. The current study employed a person-centered approach to explore emerging patterns across ethnic identity development and psychological well-being criterion variables among college freshmen. Data from 283 incoming college freshmen at Northeastern University were analyzed. The Brief COPE Acceptance and Emotional Support scales, the Five Factor Mindfulness Questionnaire, and MIEM Exploration and Affirmation measures were used to inform the cluster profiles. The TwoStep auto-clustering algorithm revealed an optimal three-cluster solution (BIC = 848.49), which classified 92.6% (n = 262) of participants in the sample into one of the three clusters. The clusters were characterized as ‘Mixed Adjustment’, ‘Lowest Adjustment’, and ‘Moderate Adjustment.’ Cluster composition varied significantly by ethnicity X² (2, N = 262) = 7.74 (p = .021) and gender X² (2, N = 259) = 10.40 (p = .034). The ‘Lowest Adjustment’ cluster contained the highest proportion of students of color, 41% (n = 32), and male-identifying students, 44.2% (n = 34). Follow-up analyses showed higher ERI exploration in ‘Moderate Adjustment’ cluster members, also reported higher levels of psychological distress, with significantly elevated depression scores (p = .011), psychological diagnoses of depression (p = .013), anxiety (p = .005) and psychiatric disorders (p = .025). Supporting prior research, students engaging with identity exploration processes often endure more psychological distress. These results indicate that students undergoing identity development may require more socialization and different services beyond normal strategies.

Keywords: adjustment, coping, college, emerging adulthood, ethnic-racial identity, psychological well-being, resilience

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Authors: Gurkirat Kaur, Manoj Kumar, Manju Bala

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Ethernet is the most influential and widely used technology in the world. With the growing demand of low latency and high throughput technologies like InfiniBand and RoCE, unique features viz. RDMA (Remote Direct Memory Access) have evolved. RDMA is an effective technology which is used for reducing system load and improving performance. InfiniBand is a well known technology which provides high-bandwidth and low-latency and makes optimal use of in-built features like RDMA. With the rapid evolution of InfiniBand technology and Ethernet lacking the RDMA and zero copy protocol, the Ethernet community has came out with a new enhancements that bridges the gap between InfiniBand and Ethernet. By adding the RDMA and zero copy protocol to the Ethernet a new networking technology is evolved, called RDMA over Converged Ethernet (RoCE). RoCE is a standard released by the IBTA standardization body to define RDMA protocol over Ethernet. With the emergence of lossless Ethernet, RoCE uses InfiniBand’s efficient transport to provide the platform for deploying RDMA technology in mainstream data centres over 10GigE, 40GigE and beyond. RoCE provide all of the InfiniBand benefits transport benefits and well established RDMA ecosystem combined with converged Ethernet. In this paper, we evaluate the heterogeneous Linux cluster, having multi nodes with fast interconnects i.e. gigabit Ethernet and Soft RoCE. This paper presents the heterogeneous Linux cluster configuration and evaluates its performance using Intel’s MPI Benchmarks. Our result shows that Soft RoCE is performing better than Ethernet in various performance metrics like bandwidth, latency and throughput.

Keywords: ethernet, InfiniBand, RoCE, RDMA, MPI, Soft RoCE

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Authors: Rute Faria, Digo Moraes, Amanda Santos, Dione Morais, Maria Sartori

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Seed vigor is a fundamental component for the good performance of the entire soybean production process. Seeds with mechanical damage at harvest time will be more susceptible to fungal and insect attack during storage, which will invariably reduce their vigor to the field, compromising uniformity and final stand performance. Harvesters, even the most modern ones, when not properly regulated or operated, can cause irreversible damages to the seeds, compromising even their commercialization. Therefore, the control of an efficient harvest is necessary in order to guarantee a good quality final product. In this work, the damage caused by two different harvesters (one rented, and another one) was evaluated, traveling in two speeds (4 and 8 km / h). The design was completely randomized in 2 x 2 factorial, with four replications. To evaluate the physiological quality seed germination and vigor tests were carried out over a period of six months. A multivariate analysis of Principal Components (PCA) and clustering allowed us to verify that the leased machine had better performance in the incidence of immediate damages in the seeds, but after a storage period of 6 months the vigor of these seeds reduced more than own machine evidencing that such a machine would bring more damages to the seeds.

Keywords: Glycine max (L.), cluster analysis, PCA, vigor

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Authors: Forrest Kaatz, Adhemar Bultheel

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We use an informational statistical mechanics approach to study the catalytic thermodynamics of platinum and palladium cuboctahedral nanoclusters. Nanoclusters and their adatoms are viewed as chemical graphs with a nearest neighbor adjacency matrix. We use the Morse potential to determine bond energies between cluster atoms in a coordination type calculation. We use adsorbate energies calculated from density functional theory (DFT) to study the adatom effects on the thermodynamic quantities, which are derived from a Hamiltonian. Oxygen radical and molecular adsorbates are studied on platinum clusters and hydrogen on palladium clusters. We calculate the entropy, free energy, and total energy as the coverage of adsorbates increases from bridge and hollow sites on the surface. Thermodynamic behavior versus adatom coverage is related to the structural distribution of adatoms on the nanocluster surfaces. The thermodynamic functions are characterized using a simple adsorption model, with linear trends as the coverage of adatoms increases. The data exhibits size effects for the measured thermodynamic properties with cluster diameters between 2 and 5 nm. Entropy and enthalpy calculations of Pt-O2 compare well with previous theoretical data for Pt(111)-O2, and our Pd-H results show similar trends as experimental measurements for Pd-H2 nanoclusters. Our methods are general and may be applied to wide variety of nanocluster adsorbate systems.

Keywords: catalytic thermodynamics, palladium nanocluster absorbates, platinum nanocluster absorbates, statistical mechanics

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Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton

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Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.

Keywords: pollen recognition, logistic model tree, expectation-maximization, local binary pattern

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Authors: E. Izsó, M. Bartalné-Berceli, Sz. Gergely, A. Salgó

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The aims of this investigation is to elaborate near-infrared methods for testing and recognition of chemical components and quality in “Pannon wheat” allied (i.e. true to variety or variety identified) milling fractions as well as to develop spectroscopic methods following the milling processes and evaluate the stability of the milling technology by different types of milling products and according to sampling times, respectively. This wheat categories produced under industrial conditions where samples were collected versus sampling time and maximum or minimum yields. The changes of the main chemical components (such as starch, protein, lipid) and physical properties of fractions (particle size) were analysed by dispersive spectrophotometers using visible (VIS) and near-infrared (NIR) regions of the electromagnetic radiation. Close correlation were obtained between the data of spectroscopic measurement techniques processed by various chemometric methods (e.g. principal component analysis (PCA), cluster analysis (CA) and operation condition of milling technology. Its obvious that NIR methods are able to detect the deviation of the yield parameters and differences of the sampling times by a wide variety of fractions, respectively. NIR technology can be used in the sensitive monitoring of milling technology.

Keywords: near infrared spectroscopy, wheat categories, milling process, monitoring

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Authors: İsmail Kavdır, M. Burak Büyükcan, Ferhat Kurtulmuş

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Table olive is a valuable product especially in Mediterranean countries. It is usually consumed after some fermentation process. Defects happened naturally or as a result of an impact while olives are still fresh may become more distinct after processing period. Defected olives are not desired both in table olive and olive oil industries as it will affect the final product quality and reduce market prices considerably. Therefore it is critical to sort table olives before processing or even after processing according to their quality and surface defects. However, doing manual sorting has many drawbacks such as high expenses, subjectivity, tediousness and inconsistency. Quality criterions for green olives were accepted as color and free of mechanical defects, wrinkling, surface blemishes and rotting. In this study, it was aimed to classify fresh table olives using different classifiers and NIR spectroscopy readings and also to compare the classifiers. For this purpose, green (Ayvalik variety) olives were classified based on their surface feature properties such as defect-free, with bruised defect and with fly defect using FT-NIR spectroscopy and classification algorithms such as artificial neural networks, ident and cluster. Bruker multi-purpose analyzer (MPA) FT-NIR spectrometer (Bruker Optik, GmbH, Ettlingen Germany) was used for spectral measurements. The spectrometer was equipped with InGaAs detectors (TE-InGaAs internal for reflectance and RT-InGaAs external for transmittance) and a 20-watt high intensity tungsten–halogen NIR light source. Reflectance measurements were performed with a fiber optic probe (type IN 261) which covered the wavelengths between 780–2500 nm, while transmittance measurements were performed between 800 and 1725 nm. Thirty-two scans were acquired for each reflectance spectrum in about 15.32 s while 128 scans were obtained for transmittance in about 62 s. Resolution was 8 cm⁻¹ for both spectral measurement modes. Instrument control was done using OPUS software (Bruker Optik, GmbH, Ettlingen Germany). Classification applications were performed using three classifiers; Backpropagation Neural Networks, ident and cluster classification algorithms. For these classification applications, Neural Network tool box in Matlab, ident and cluster modules in OPUS software were used. Classifications were performed considering different scenarios; two quality conditions at once (good vs bruised, good vs fly defect) and three quality conditions at once (good, bruised and fly defect). Two spectrometer readings were used in classification applications; reflectance and transmittance. Classification results obtained using artificial neural networks algorithm in discriminating good olives from bruised olives, from olives with fly defect and from the olive group including both bruised and fly defected olives with success rates respectively changing between 97 and 99%, 61 and 94% and between 58.67 and 92%. On the other hand, classification results obtained for discriminating good olives from bruised ones and also for discriminating good olives from fly defected olives using the ident method ranged between 75-97.5% and 32.5-57.5%, respectfully; results obtained for the same classification applications using the cluster method ranged between 52.5-97.5% and between 22.5-57.5%.

Keywords: artificial neural networks, statistical classifiers, NIR spectroscopy, reflectance, transmittance

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Authors: Kristian Bautista, Ruben A. Idoy

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A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.

Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization

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Authors: Ashfaque Hossain Khan, Brian Thomson, Ratan Debnath, Abhishek Motayed, Mulpuri V. Rao

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Though the efforts had been made, the problem of cross-sensitivity for a single metal oxide-based sensor can’t be fully eliminated. In this work, a sensor array has been designed and fabricated comprising of platinum (Pt), copper (Cu), and silver (Ag) decorated TiO2 and ZnO functionalized GaN nanowires using industry-standard top-down fabrication approach. The metal/metal-oxide combinations within the array have been determined from prior molecular simulation study using first principle calculations based on density functional theory (DFT). The gas responses were obtained for both single and mixture of NO2, SO2, ethanol, and H2 in the presence of H2O and O2 gases under UV light at room temperature. Each gas leaves a unique response footprint across the array sensors by which precise discrimination of cross-sensitive gases has been achieved. An unsupervised principal component analysis (PCA) technique has been implemented on the array response. Results indicate that each gas forms a distinct cluster in the score plot for all the target gases and their mixtures, indicating a clear separation among them. In addition, the developed array device consumes very low power because of ultra-violet (UV) assisted sensing as compared to commercially available metal-oxide sensors. The nanowire sensor array, in combination with PCA, is a potential approach for precise real-time gas monitoring applications.

Keywords: cross-sensitivity, gas sensor, principle component analysis (PCA), sensor array

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Authors: Anuj Abraham, N. Pappa, Daniel Honc, Rahul Sharma

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In this paper, an analysis of some model order reduction techniques is presented. A new hybrid algorithm for model order reduction of linear time invariant systems is compared with the conventional techniques namely Balanced Truncation, Hankel Norm reduction and Dominant Pole Algorithm (DPA). The proposed hybrid algorithm is known as Clustering Dominant Pole Algorithm (CDPA) is able to compute the full set of dominant poles and its cluster center efficiently. The dominant poles of a transfer function are specific eigenvalues of the state space matrix of the corresponding dynamical system. The effectiveness of this novel technique is shown through the simulation results.

Keywords: balanced truncation, clustering, dominant pole, Hankel norm, model reduction

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Authors: Ganesh B. Shinde, Vijaya B. Musande

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Cotton crop identification based on the timely information has significant advantage to the different implications of food, economic and environment. Due to the significant advantages, the accurate detection of cotton crop regions using supervised learning procedure is challenging problem in remote sensing. Here, classifiers on the direct image are played a major role but the results are not much satisfactorily. In order to further improve the effectiveness, variety of vegetation indices are proposed in the literature. But, recently, the major challenge is to find the better vegetation indices for the cotton crop identification through the proposed methodology. Accordingly, fuzzy c-means clustering is combined with neural network algorithm, trained by Levenberg-Marquardt for cotton crop classification. To experiment the proposed method, five LISS-III satellite images was taken and the experimentation was done with six vegetation indices such as Simple Ratio, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Green Atmospherically Resistant Vegetation Index, Wide-Dynamic Range Vegetation Index, Green Chlorophyll Index. Along with these indices, Green Leaf Area Index is also considered for investigation. From the research outcome, Green Atmospherically Resistant Vegetation Index outperformed with all other indices by reaching the average accuracy value of 95.21%.

Keywords: Fuzzy C-Means clustering (FCM), neural network, Levenberg-Marquardt (LM) algorithm, vegetation indices

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Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

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This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

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Authors: Kyonghoon Kim, Wonyoung Park, Taeil Park

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In 1994, Korea experienced a national tragedy of Seongsu Bridge collapse. The accident was severe enough to alert governmental officers to the problem of existing management policy for national infrastructures. As a result, government legislated the ‘Guidelines for the safety inspection and test of infrastructure’ which have been utilized as the primary tool to make decision for the maintenance and rehabilitation of infrastructure for last twenty years. Although it is clear that the guideline established a basics how to evaluate and manage the condition of infrastructures in systematic manner, it is equally clear that the guideline needs improvements in order to obtain reasonable investment decisions for budget allocation. Because its inspection and evaluation procedures mainly focused on the structural condition of infrastructures, it was hard to make decision when the infrastructures were in same level of structural condition. In addition, it did not properly reflect various aspects of infrastructures such as performance, public demand, capacity, etc., which were more valuable to public. Regardless of the importance, these factors were commonly neglected in governmental decision-making process, because there factors were somewhat subjective and difficult to quantify in rational manner. Thus, this study proposes a framework to properly evaluate the serviceability indicators using AHP and Fuzzy approach. The framework is expected to assist governmental agency in establishing effective investment strategies for budget planning.

Keywords: infrastructure, evaluation, serviceability, fuzzy

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Authors: Josef Voracek, Jan Sima

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The main purpose of our study is to state the basic types of fitness customers. This paper aims to create a specific customer typology in today’s fitness centres in the region of Prague. Our suggested typology of Prague fitness centres customers is based on answers to the questions: What are the customers like, what are their preferences, and what kinds of services do they use more often in Prague fitness centres? These are the main aspects of the presented typology. A survey was conducted on a sample of 1004 respondents from 48 fitness centres, which ran during May 2012. We used questionnaires and latent class analysis for the assessment and interpretation of data. Gender was especially the main filter criterion. In the population, there were 522 males and 482 females. Data were analysed using the LCA method. We identified 6 segments of typical customers, of which three are male and three are female. Each segment is influenced primarily by the age of customers, from which we can develop further characteristics, such as education, income, marital status, etc. Male segments use the main workout area above all, whilst female segments use a much wider range of services offered, for example, group exercises, personal training, and cardio theatres. LCA method was found to be the most suitable tool, because cluster analysis is very limited in the forms and numbers of variables and indicators. Models of 3 latent classes for each gender are optimal, as it is demonstrated by entropy indices and matrices of the likelihood of the membership to the classes. A probable weak point of the survey is the selection of fitness centres, because of the market in Prague is really specific.

Keywords: customer, fitness, latent class analysis, typology

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Authors: O. P. Rahi, Manoj Kumar

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Power transformers are essential and expensive equipments in electrical power system. Dissolved gas analysis (DGA) is one of the most useful techniques to detect incipient faults in power transformers. However, the identification of the faulted location by conventional method is not always an easy task due to variability of gas data and operational variables. In this paper, an extension theory based power transformer fault diagnosis method is presented. Extension theory tries to solve contradictions and incompatibility problems. This paper first briefly introduces the basic concept of matter element theory, establishes the matter element models for three-ratio method, and then briefly discusses extension set theory. Detailed analysis is carried out on the extended relation function (ERF) adopted in this paper for transformer fault diagnosis. The detailed diagnosing steps are offered. Simulation proves that the proposed method can overcome the drawbacks of the conventional three-ratio method, such as no matching and failure to diagnose multi-fault. It enhances diagnosing accuracy.

Keywords: DGA, extension theory, ERF, fault diagnosis power transformers, fault diagnosis, fuzzy logic

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Authors: Kaining Zhang, Lang Chen, Danyang Liu, Jianying Lu, Kun Yang, Junying Wu

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In order to solve the problem of a large amount of simulation and limited simulation scale in the first-principle molecular dynamics simulation of energetic material detonation reaction, we established an artificial intelligence model for analyzing and predicting the detonation reaction mechanism of CL-20 based on the first-principle molecular dynamics simulation of the multiscale shock technique (MSST). We employed principal component analysis to identify the dominant charge features governing molecular reactions. We adopted the K-means clustering algorithm to cluster the reaction paths and screen out the key reactions. We introduced the neural network algorithm to construct the mapping relationship between the charge characteristics of the molecular structure and the key reaction characteristics so as to establish a calculation method for predicting detonation reactions based on the charge characteristics of CL-20 and realize the rapid analysis of the reaction mechanism of energetic materials.

Keywords: energetic material detonation reaction, first-principle molecular dynamics simulation of multiscale shock technique, neural network, CL-20

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Authors: A. Assad, T. Zayed

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Water networks (WNs) are responsible of providing adequate amounts of safe, high quality, water to the public. As other critical infrastructure systems, WNs are subjected to deterioration which increases the number of breaks and leaks and lower water quality. In Canada, 35% of water assets require critical attention and there is a significant gap between the needed and the implemented investments. Thus, the need for efficient rehabilitation programs is becoming more urgent given the paradigm of aging infrastructure and tight budget. The first step towards developing such programs is to formulate a Performance Index that reflects the current condition of water assets along with its criticality. While numerous studies in the literature have focused on various aspects of condition assessment and reliability, limited efforts have investigated the criticality of such components. Critical water mains are those whose failure cause significant economic, environmental or social impacts on a community. Inclusion of criticality in computing the performance index will serve as a prioritizing tool for the optimum allocating of the available resources and budget. In this study, several social, economic, and environmental factors that dictate the criticality of a water pipelines have been elicited from analyzing the literature. Expert opinions were sought to provide pairwise comparisons of the importance of such factors. Subsequently, Fuzzy Logic along with Analytical Network Process (ANP) was utilized to calculate the weights of several criteria factors. Multi Attribute Utility Theories (MAUT) was then employed to integrate the aforementioned weights with the attribute values of several pipelines in Montreal WN. The result is a criticality index, 0-1, that quantifies the severity of the consequence of failure of each pipeline. A novel contribution of this approach is that it accounts for both the interdependency between criteria factors as well as the inherited uncertainties in calculating the criticality. The practical value of the current study is represented by the automated tool, Excel-MATLAB, which can be used by the utility managers and decision makers in planning for future maintenance and rehabilitation activities where high-level efficiency in use of materials and time resources is required.

Keywords: water networks, criticality assessment, asset management, fuzzy analytical network process

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Authors: Rik van Leeuwen, Ger Koole

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Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

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Authors: Jessica Carolina Hernandez Galeano, Juan Carlos Moreno Pirajan, Liliana Giraldo

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Carbon gels are materials whose structure and porous texture can be designed and controlled on a nanoscale. Among their characteristics it is found their low density, large surface area and high degree of porosity. These materials are produced by a sol-gel polymerization of organic monomers using basic or acid catalysts, followed by drying and controlled carbonization. In this work, the synthesis and characterization of carbon aerogels from resorcinol, phenol and formaldehyde in ethanol is described. The aim of this study is obtaining different carbonaceous materials in the form of spheres using the Stöber method to perform a further evaluation of CO₂ adsorption of each material. In general, the synthesis consisted of a sol-gel polymerization process that generates a cluster (cross-linked organic monomers) from the precursors in the presence of NH₃ as a catalyst. This cluster was subjected to specific conditions of gelling and curing (30°C for 24 hours and 100°C for 24 hours, respectively) and CO₂ supercritical drying. Finally, the dry material was subjected to a process of carbonization or pyrolysis, in N₂ atmosphere at 350°C (1° C / min) for 2 h and 600°C (1°C / min) for 4 hours, to obtain porous solids that retain the structure initially desired. For this work, both the concentrations of the precursors and the proportion of ammonia in the medium where modify to describe the effect of the use of phenol and the amount of catalyst in the resulting material. Carbon aerogels were characterized by Scanning Electron Microscope (SEM), N₂ isotherms, infrared spectroscopy (IR) and X-ray Powder Diffraction (XRD) showing the obtention of carbon spheres in the nanometric scale with BET areas around 500 m2g-1.

Keywords: carbon aerogels, carbon spheres, CO₂ adsorption, Stöber method

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Authors: Pei-Syuan Ruan, Ya-Chi Yu, Shao-Wen Chen, Jin-Der Lee, Jong-Rong Wang, Chunkuan Shih

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This study investigated the void fraction characteristics under low superficial gas velocity (J_g) and low superficial fluid velocity (J_f) conditions in a 3x3 rod bundle geometry. Three arrangements of conductivity probes were set to measure the void fraction at various cross-sectional regions, including rod-gap, sub-channel and rod-wall regions. The experimental tests were performed under the flow conditions of J_g = 0-0.236 m/s and J_f = 0-0.142 m/s, and the time-averaged void fractions were recorded at each flow condition. It was observed that while the superficial gas velocity increases, the small bubbles started to cluster together and become big bubbles. As the superficial fluid velocity increases, the local void fractions of the three test regions will get closer and the bubble distribution will be more uniform across the cross section.

Keywords: conductivity probes, rod bundles, two-phase flow, void fraction

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Authors: I. V. Pinto, M. R. Sooriyarachchi

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It can be frequently observed that the data arising in our environment have a hierarchical or a nested structure attached with the data. Multilevel modelling is a modern approach to handle this kind of data. When multilevel modelling is combined with a binary response, the estimation methods get complex in nature and the usual techniques are derived from quasi-likelihood method. The estimation methods which are compared in this study are, marginal quasi-likelihood (order 1 & order 2) (MQL1, MQL2) and penalized quasi-likelihood (order 1 & order 2) (PQL1, PQL2). A statistical model is of no use if it does not reflect the given dataset. Therefore, checking the adequacy of the fitted model through a goodness-of-fit (GOF) test is an essential stage in any modelling procedure. However, prior to usage, it is also equally important to confirm that the GOF test performs well and is suitable for the given model. This study assesses the suitability of the GOF test developed for binary response multilevel models with respect to the method used in model estimation. An extensive set of simulations was conducted using MLwiN (v 2.19) with varying number of clusters, cluster sizes and intra cluster correlations. The test maintained the desirable Type-I error for models estimated using PQL2 and it failed for almost all the combinations of MQL. Power of the test was adequate for most of the combinations in all estimation methods except MQL1. Moreover, models were fitted using the four methods to a real-life dataset and performance of the test was compared for each model.

Keywords: goodness-of-fit test, marginal quasi-likelihood, multilevel modelling, penalized quasi-likelihood, power, quasi-likelihood, type-I error

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Authors: Gang Wang

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Education is widely believed to be a prerequisite for democracy and civil society, but the causal link between education and outcome variables is usually hardly to be identified. This study applies a fuzzy regression discontinuity design to examine the effects of college education on democratic attitudes in the Chinese context. In the analysis treatment assignment is determined by students’ college entry years and thus naturally selected by subjects’ ages. Using a sample of Chinese college students collected in Beijing in 2009, this study finds that college education actually reduces undergraduates’ motivation for political development in China but promotes political loyalty to the authoritarian government. Further hypotheses tests explain these interesting findings from two perspectives. The first is related to the complexity of politics. As college students progress over time, they increasingly realize the complexity of political reform in China’s authoritarian regime and rather stay away from politics. The second is related to students’ career opportunities. As students are close to graduation, they are immersed with job hunting and have a reduced interest in political freedom.

Keywords: china, college education, democratic attitudes, regression discontinuity

Procedia PDF Downloads 347